Overview

Dataset statistics

Number of variables22
Number of observations441564
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory74.1 MiB
Average record size in memory176.0 B

Variable types

Unsupported1
Categorical1
Numeric20

Alerts

LAT is highly correlated with LON and 5 other fieldsHigh correlation
LON is highly correlated with LATHigh correlation
PRECTOT is highly correlated with QV2M and 3 other fieldsHigh correlation
PS is highly correlated with LAT and 5 other fieldsHigh correlation
QV2M is highly correlated with PRECTOT and 7 other fieldsHigh correlation
RH2M is highly correlated with PRECTOT and 3 other fieldsHigh correlation
T2M is highly correlated with LAT and 6 other fieldsHigh correlation
T2MWET is highly correlated with PRECTOT and 7 other fieldsHigh correlation
T2M_MAX is highly correlated with LAT and 6 other fieldsHigh correlation
T2M_MIN is highly correlated with LAT and 6 other fieldsHigh correlation
T2M_RANGE is highly correlated with PRECTOT and 3 other fieldsHigh correlation
TS is highly correlated with LAT and 6 other fieldsHigh correlation
WS10M is highly correlated with WS10M_MAX and 4 other fieldsHigh correlation
WS10M_MAX is highly correlated with WS10M and 4 other fieldsHigh correlation
WS10M_MIN is highly correlated with WS50M and 1 other fieldsHigh correlation
WS10M_RANGE is highly correlated with PS and 4 other fieldsHigh correlation
WS50M is highly correlated with WS10M and 4 other fieldsHigh correlation
WS50M_MAX is highly correlated with WS10M and 4 other fieldsHigh correlation
WS50M_MIN is highly correlated with WS10M_MIN and 1 other fieldsHigh correlation
WS50M_RANGE is highly correlated with WS10M and 3 other fieldsHigh correlation
LAT is highly correlated with LON and 5 other fieldsHigh correlation
LON is highly correlated with LATHigh correlation
PS is highly correlated with LAT and 6 other fieldsHigh correlation
QV2M is highly correlated with RH2M and 6 other fieldsHigh correlation
RH2M is highly correlated with QV2M and 2 other fieldsHigh correlation
T2M is highly correlated with LAT and 6 other fieldsHigh correlation
T2MWET is highly correlated with PS and 7 other fieldsHigh correlation
T2M_MAX is highly correlated with LAT and 6 other fieldsHigh correlation
T2M_MIN is highly correlated with LAT and 6 other fieldsHigh correlation
T2M_RANGE is highly correlated with QV2M and 2 other fieldsHigh correlation
TS is highly correlated with LAT and 6 other fieldsHigh correlation
WS10M is highly correlated with WS10M_MAX and 6 other fieldsHigh correlation
WS10M_MAX is highly correlated with WS10M and 4 other fieldsHigh correlation
WS10M_MIN is highly correlated with WS10M and 3 other fieldsHigh correlation
WS10M_RANGE is highly correlated with PS and 4 other fieldsHigh correlation
WS50M is highly correlated with WS10M and 5 other fieldsHigh correlation
WS50M_MAX is highly correlated with WS10M and 6 other fieldsHigh correlation
WS50M_MIN is highly correlated with WS10M and 3 other fieldsHigh correlation
WS50M_RANGE is highly correlated with WS10M and 4 other fieldsHigh correlation
LAT is highly correlated with LON and 1 other fieldsHigh correlation
LON is highly correlated with LATHigh correlation
PRECTOT is highly correlated with QV2MHigh correlation
PS is highly correlated with LAT and 3 other fieldsHigh correlation
QV2M is highly correlated with PRECTOT and 5 other fieldsHigh correlation
RH2M is highly correlated with QV2M and 1 other fieldsHigh correlation
T2M is highly correlated with PS and 4 other fieldsHigh correlation
T2MWET is highly correlated with QV2M and 3 other fieldsHigh correlation
T2M_MAX is highly correlated with PS and 3 other fieldsHigh correlation
T2M_MIN is highly correlated with PS and 5 other fieldsHigh correlation
T2M_RANGE is highly correlated with QV2M and 1 other fieldsHigh correlation
TS is highly correlated with QV2M and 4 other fieldsHigh correlation
WS10M is highly correlated with WS10M_MAX and 2 other fieldsHigh correlation
WS10M_MAX is highly correlated with WS10M and 3 other fieldsHigh correlation
WS10M_MIN is highly correlated with WS50M_MINHigh correlation
WS10M_RANGE is highly correlated with WS10M_MAX and 1 other fieldsHigh correlation
WS50M is highly correlated with WS10M and 1 other fieldsHigh correlation
WS50M_MAX is highly correlated with WS10M and 3 other fieldsHigh correlation
WS50M_MIN is highly correlated with WS10M_MINHigh correlation
WS50M_RANGE is highly correlated with WS10M_MAX and 2 other fieldsHigh correlation
DISTRICT is highly correlated with LAT and 9 other fieldsHigh correlation
LAT is highly correlated with DISTRICT and 6 other fieldsHigh correlation
LON is highly correlated with DISTRICT and 6 other fieldsHigh correlation
PS is highly correlated with DISTRICT and 11 other fieldsHigh correlation
QV2M is highly correlated with DISTRICT and 8 other fieldsHigh correlation
RH2M is highly correlated with QV2M and 2 other fieldsHigh correlation
T2M is highly correlated with DISTRICT and 8 other fieldsHigh correlation
T2MWET is highly correlated with DISTRICT and 8 other fieldsHigh correlation
T2M_MAX is highly correlated with DISTRICT and 9 other fieldsHigh correlation
T2M_MIN is highly correlated with DISTRICT and 9 other fieldsHigh correlation
T2M_RANGE is highly correlated with QV2M and 4 other fieldsHigh correlation
TS is highly correlated with DISTRICT and 9 other fieldsHigh correlation
WS10M is highly correlated with WS10M_MAX and 6 other fieldsHigh correlation
WS10M_MAX is highly correlated with PS and 7 other fieldsHigh correlation
WS10M_MIN is highly correlated with WS10M and 4 other fieldsHigh correlation
WS10M_RANGE is highly correlated with DISTRICT and 6 other fieldsHigh correlation
WS50M is highly correlated with PS and 8 other fieldsHigh correlation
WS50M_MAX is highly correlated with WS10M and 6 other fieldsHigh correlation
WS50M_MIN is highly correlated with WS10M and 4 other fieldsHigh correlation
WS50M_RANGE is highly correlated with WS10M and 4 other fieldsHigh correlation
DISTRICT is uniformly distributed Uniform
DATE is an unsupported type, check if it needs cleaning or further analysis Unsupported
PRECTOT has 186470 (42.2%) zeros Zeros

Reproduction

Analysis started2022-01-10 11:42:12.605956
Analysis finished2022-01-10 11:45:24.522713
Duration3 minutes and 11.92 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

DATE
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size3.4 MiB

DISTRICT
Categorical

HIGH CORRELATION
UNIFORM

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
Lamjung
 
14244
Rukum
 
14244
Terhathum
 
14244
Taplejung
 
14244
Tanahun
 
14244
Other values (26)
370344 

Length

Max length13
Median length7
Mean length7.612903226
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLamjung
2nd rowLamjung
3rd rowLamjung
4th rowLamjung
5th rowLamjung

Common Values

ValueCountFrequency (%)
Lamjung14244
 
3.2%
Rukum14244
 
3.2%
Terhathum14244
 
3.2%
Taplejung14244
 
3.2%
Tanahun14244
 
3.2%
Syangja14244
 
3.2%
Surkhet14244
 
3.2%
Sunsari14244
 
3.2%
Solukhumbu14244
 
3.2%
Sindhuli14244
 
3.2%
Other values (21)299124
67.7%

Length

2022-01-10T17:30:24.936010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lamjung14244
 
3.2%
mahottari14244
 
3.2%
makwanpur14244
 
3.2%
manang14244
 
3.2%
morang14244
 
3.2%
mugu14244
 
3.2%
mustang14244
 
3.2%
myagdi14244
 
3.2%
nawalparasi14244
 
3.2%
nuwakot14244
 
3.2%
Other values (21)299124
67.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LAT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.68064516
Minimum26.5
Maximum29.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-01-10T17:30:25.051984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum26.5
5-th percentile26.6
Q127.1
median27.6
Q328.3
95-th percentile28.9
Maximum29.5
Range3
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.7506647948
Coefficient of variation (CV)0.02711876079
Kurtosis-0.6489297742
Mean27.68064516
Median Absolute Deviation (MAD)0.6
Skewness0.2924246077
Sum12222776.4
Variance0.5634976341
MonotonicityNot monotonic
2022-01-10T17:30:25.158127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2842732
 
9.7%
27.428488
 
6.5%
28.628488
 
6.5%
26.928488
 
6.5%
28.428488
 
6.5%
26.628488
 
6.5%
27.328488
 
6.5%
27.128488
 
6.5%
28.314244
 
3.2%
28.114244
 
3.2%
Other values (12)170928
38.7%
ValueCountFrequency (%)
26.514244
3.2%
26.628488
6.5%
26.714244
3.2%
26.814244
3.2%
26.928488
6.5%
27.128488
6.5%
27.214244
3.2%
27.328488
6.5%
27.428488
6.5%
27.514244
3.2%
ValueCountFrequency (%)
29.514244
 
3.2%
28.914244
 
3.2%
28.628488
6.5%
28.514244
 
3.2%
28.428488
6.5%
28.314244
 
3.2%
28.214244
 
3.2%
28.114244
 
3.2%
2842732
9.7%
27.914244
 
3.2%

LON
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.03548387
Minimum81.7
Maximum87.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-01-10T17:30:25.268522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum81.7
5-th percentile82.1
Q183.7
median85.2
Q386.8
95-th percentile87.7
Maximum87.8
Range6.1
Interquartile range (IQR)3.1

Descriptive statistics

Standard deviation1.776651339
Coefficient of variation (CV)0.02089305849
Kurtosis-1.07713805
Mean85.03548387
Median Absolute Deviation (MAD)1.5
Skewness-0.1169813934
Sum37548608.4
Variance3.156489979
MonotonicityNot monotonic
2022-01-10T17:30:25.393942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
87.328488
 
6.5%
82.128488
 
6.5%
83.828488
 
6.5%
86.528488
 
6.5%
86.828488
 
6.5%
85.328488
 
6.5%
84.414244
 
3.2%
82.414244
 
3.2%
87.714244
 
3.2%
84.114244
 
3.2%
Other values (15)213660
48.4%
ValueCountFrequency (%)
81.714244
3.2%
82.128488
6.5%
82.414244
3.2%
83.414244
3.2%
83.514244
3.2%
83.614244
3.2%
83.714244
3.2%
83.828488
6.5%
8414244
3.2%
84.114244
3.2%
ValueCountFrequency (%)
87.814244
3.2%
87.714244
3.2%
87.514244
3.2%
87.328488
6.5%
87.114244
3.2%
86.828488
6.5%
86.528488
6.5%
85.914244
3.2%
85.814244
3.2%
85.514244
3.2%

PRECTOT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct5064
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.493050294
Minimum0
Maximum177.79
Zeros186470
Zeros (%)42.2%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-01-10T17:30:25.532141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.06
Q32.01
95-th percentile13.2
Maximum177.79
Range177.79
Interquartile range (IQR)2.01

Descriptive statistics

Standard deviation6.159693069
Coefficient of variation (CV)2.47074561
Kurtosis45.02434558
Mean2.493050294
Median Absolute Deviation (MAD)0.06
Skewness5.297371359
Sum1100841.26
Variance37.94181871
MonotonicityNot monotonic
2022-01-10T17:30:25.759255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0186470
42.2%
0.0111805
 
2.7%
0.027847
 
1.8%
0.034702
 
1.1%
0.044398
 
1.0%
0.053404
 
0.8%
0.063268
 
0.7%
0.072746
 
0.6%
0.082629
 
0.6%
0.12230
 
0.5%
Other values (5054)212065
48.0%
ValueCountFrequency (%)
0186470
42.2%
0.0111805
 
2.7%
0.027847
 
1.8%
0.034702
 
1.1%
0.044398
 
1.0%
0.053404
 
0.8%
0.063268
 
0.7%
0.072746
 
0.6%
0.082629
 
0.6%
0.092060
 
0.5%
ValueCountFrequency (%)
177.791
< 0.1%
162.611
< 0.1%
137.131
< 0.1%
133.321
< 0.1%
129.491
< 0.1%
128.671
< 0.1%
124.761
< 0.1%
119.981
< 0.1%
115.781
< 0.1%
114.41
< 0.1%

PS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3105
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.34443974
Minimum54.73
Maximum100.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-01-10T17:30:25.949672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum54.73
5-th percentile61.43
Q177.94
median83.92
Q393.37
95-th percentile97.73
Maximum100.34
Range45.61
Interquartile range (IQR)15.43

Descriptive statistics

Standard deviation11.48917374
Coefficient of variation (CV)0.1378517124
Kurtosis-0.3295566016
Mean83.34443974
Median Absolute Deviation (MAD)8.7
Skewness-0.7478136617
Sum36801904.19
Variance132.0011131
MonotonicityNot monotonic
2022-01-10T17:30:26.116144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94.4636
 
0.1%
94.24615
 
0.1%
79.63612
 
0.1%
94.38611
 
0.1%
94.48603
 
0.1%
80.17600
 
0.1%
79.69596
 
0.1%
94.42591
 
0.1%
79.56590
 
0.1%
94.32588
 
0.1%
Other values (3095)435522
98.6%
ValueCountFrequency (%)
54.731
 
< 0.1%
54.751
 
< 0.1%
54.892
< 0.1%
54.931
 
< 0.1%
54.952
< 0.1%
54.961
 
< 0.1%
54.982
< 0.1%
54.991
 
< 0.1%
553
< 0.1%
55.011
 
< 0.1%
ValueCountFrequency (%)
100.341
 
< 0.1%
100.241
 
< 0.1%
100.221
 
< 0.1%
100.22
< 0.1%
100.192
< 0.1%
100.171
 
< 0.1%
100.163
< 0.1%
100.151
 
< 0.1%
100.143
< 0.1%
100.131
 
< 0.1%

QV2M
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2216
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.690246963
Minimum0.37
Maximum23.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-01-10T17:30:26.250758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.37
5-th percentile1.98
Q14
median7.02
Q313.56
95-th percentile18.76
Maximum23.27
Range22.9
Interquartile range (IQR)9.56

Descriptive statistics

Standard deviation5.521436277
Coefficient of variation (CV)0.6353601113
Kurtosis-0.9843009085
Mean8.690246963
Median Absolute Deviation (MAD)3.8
Skewness0.5493849208
Sum3837300.21
Variance30.48625856
MonotonicityNot monotonic
2022-01-10T17:30:26.387020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.92590
 
0.1%
3.94579
 
0.1%
3.58578
 
0.1%
3.62576
 
0.1%
3.3576
 
0.1%
4.12573
 
0.1%
3.84565
 
0.1%
3.82565
 
0.1%
4.04562
 
0.1%
4.08556
 
0.1%
Other values (2206)435844
98.7%
ValueCountFrequency (%)
0.371
 
< 0.1%
0.381
 
< 0.1%
0.41
 
< 0.1%
0.411
 
< 0.1%
0.434
< 0.1%
0.453
< 0.1%
0.462
 
< 0.1%
0.471
 
< 0.1%
0.487
< 0.1%
0.491
 
< 0.1%
ValueCountFrequency (%)
23.271
< 0.1%
23.011
< 0.1%
22.971
< 0.1%
22.931
< 0.1%
22.91
< 0.1%
22.811
< 0.1%
22.751
< 0.1%
22.732
< 0.1%
22.671
< 0.1%
22.631
< 0.1%

RH2M
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9337
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.3655243
Minimum4.04
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-01-10T17:30:26.540887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4.04
5-th percentile20.31
Q137.38
median55.51
Q377.85
95-th percentile89.76
Maximum100
Range95.96
Interquartile range (IQR)40.47

Descriptive statistics

Standard deviation22.83592297
Coefficient of variation (CV)0.4051399017
Kurtosis-1.180197568
Mean56.3655243
Median Absolute Deviation (MAD)20.09
Skewness-0.05998050708
Sum24888986.37
Variance521.4793781
MonotonicityNot monotonic
2022-01-10T17:30:26.818496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86.94125
 
< 0.1%
86.26120
 
< 0.1%
87.16119
 
< 0.1%
87.54116
 
< 0.1%
86.6115
 
< 0.1%
84.62114
 
< 0.1%
87.12113
 
< 0.1%
82.74111
 
< 0.1%
86.02111
 
< 0.1%
83.94111
 
< 0.1%
Other values (9327)440409
99.7%
ValueCountFrequency (%)
4.041
< 0.1%
4.141
< 0.1%
4.181
< 0.1%
4.211
< 0.1%
4.251
< 0.1%
4.331
< 0.1%
4.341
< 0.1%
4.351
< 0.1%
4.381
< 0.1%
4.421
< 0.1%
ValueCountFrequency (%)
10032
< 0.1%
99.962
 
< 0.1%
99.882
 
< 0.1%
99.861
 
< 0.1%
99.841
 
< 0.1%
99.822
 
< 0.1%
99.811
 
< 0.1%
99.721
 
< 0.1%
99.712
 
< 0.1%
99.613
 
< 0.1%

T2M
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5474
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.02991743
Minimum-22.52
Maximum38.61
Zeros28
Zeros (%)< 0.1%
Negative30015
Negative (%)6.8%
Memory size3.4 MiB
2022-01-10T17:30:26.998139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-22.52
5-th percentile-2.63
Q110.08
median17.2
Q323.3
95-th percentile29.36
Maximum38.61
Range61.13
Interquartile range (IQR)13.22

Descriptive statistics

Standard deviation9.530956958
Coefficient of variation (CV)0.5945730538
Kurtosis0.1963353528
Mean16.02991743
Median Absolute Deviation (MAD)6.59
Skewness-0.6374016767
Sum7078234.46
Variance90.83914053
MonotonicityNot monotonic
2022-01-10T17:30:27.155436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.26292
 
0.1%
18.07287
 
0.1%
18.78286
 
0.1%
17.6283
 
0.1%
18.24280
 
0.1%
17.46279
 
0.1%
18.44278
 
0.1%
17.78277
 
0.1%
18.28277
 
0.1%
18.12275
 
0.1%
Other values (5464)438750
99.4%
ValueCountFrequency (%)
-22.521
< 0.1%
-21.671
< 0.1%
-20.671
< 0.1%
-20.391
< 0.1%
-19.952
< 0.1%
-19.761
< 0.1%
-19.691
< 0.1%
-19.632
< 0.1%
-19.591
< 0.1%
-19.531
< 0.1%
ValueCountFrequency (%)
38.611
< 0.1%
38.591
< 0.1%
38.41
< 0.1%
38.281
< 0.1%
38.231
< 0.1%
38.081
< 0.1%
38.051
< 0.1%
37.931
< 0.1%
37.861
< 0.1%
37.852
< 0.1%

T2MWET
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5198
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.817614185
Minimum-28.19
Maximum27.15
Zeros152
Zeros (%)< 0.1%
Negative143218
Negative (%)32.4%
Memory size3.4 MiB
2022-01-10T17:30:27.291469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-28.19
5-th percentile-12.61
Q1-2.35
median5.45
Q315.37
95-th percentile22.78
Maximum27.15
Range55.34
Interquartile range (IQR)17.72

Descriptive statistics

Standard deviation11.04948325
Coefficient of variation (CV)1.899315234
Kurtosis-0.7949964079
Mean5.817614185
Median Absolute Deviation (MAD)8.91
Skewness-0.1582916673
Sum2568848.99
Variance122.0910801
MonotonicityNot monotonic
2022-01-10T17:30:27.413496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.13208
 
< 0.1%
15.46206
 
< 0.1%
15.88201
 
< 0.1%
15.52201
 
< 0.1%
16.4198
 
< 0.1%
1.1197
 
< 0.1%
15.63197
 
< 0.1%
15.98196
 
< 0.1%
15.92195
 
< 0.1%
22.62195
 
< 0.1%
Other values (5188)439570
99.5%
ValueCountFrequency (%)
-28.191
< 0.1%
-28.151
< 0.1%
-27.311
< 0.1%
-27.291
< 0.1%
-27.151
< 0.1%
-27.131
< 0.1%
-26.911
< 0.1%
-26.91
< 0.1%
-26.721
< 0.1%
-26.691
< 0.1%
ValueCountFrequency (%)
27.151
< 0.1%
27.011
< 0.1%
271
< 0.1%
26.961
< 0.1%
26.882
< 0.1%
26.841
< 0.1%
26.831
< 0.1%
26.772
< 0.1%
26.731
< 0.1%
26.681
< 0.1%

T2M_MAX
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5527
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.01945609
Minimum-13.74
Maximum46.82
Zeros26
Zeros (%)< 0.1%
Negative10007
Negative (%)2.3%
Memory size3.4 MiB
2022-01-10T17:30:27.630544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-13.74
5-th percentile4.45
Q116.44
median22.87
Q328.43
95-th percentile35.83
Maximum46.82
Range60.56
Interquartile range (IQR)11.99

Descriptive statistics

Standard deviation9.181674648
Coefficient of variation (CV)0.4169800839
Kurtosis0.2477901152
Mean22.01945609
Median Absolute Deviation (MAD)5.91
Skewness-0.5386943016
Sum9722999.11
Variance84.30314934
MonotonicityNot monotonic
2022-01-10T17:30:27.831875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.6294
 
0.1%
23.34292
 
0.1%
23.58292
 
0.1%
22.06289
 
0.1%
22.58286
 
0.1%
22.28283
 
0.1%
22.64281
 
0.1%
22.66281
 
0.1%
22.96279
 
0.1%
21.34278
 
0.1%
Other values (5517)438709
99.4%
ValueCountFrequency (%)
-13.741
< 0.1%
-13.641
< 0.1%
-13.621
< 0.1%
-13.291
< 0.1%
-13.141
< 0.1%
-13.081
< 0.1%
-12.941
< 0.1%
-12.831
< 0.1%
-12.811
< 0.1%
-12.751
< 0.1%
ValueCountFrequency (%)
46.821
< 0.1%
46.791
< 0.1%
46.641
< 0.1%
46.61
< 0.1%
46.281
< 0.1%
46.131
< 0.1%
45.991
< 0.1%
45.981
< 0.1%
45.961
< 0.1%
45.841
< 0.1%

T2M_MIN
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5590
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.33361406
Minimum-32.6
Maximum32.77
Zeros66
Zeros (%)< 0.1%
Negative51910
Negative (%)11.8%
Memory size3.4 MiB
2022-01-10T17:30:28.018576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-32.6
5-th percentile-7.98
Q15.57
median12.53
Q318.57
95-th percentile24.48
Maximum32.77
Range65.37
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.732227901
Coefficient of variation (CV)0.858704721
Kurtosis0.3650015659
Mean11.33361406
Median Absolute Deviation (MAD)6.54
Skewness-0.7358785847
Sum5004515.96
Variance94.71625992
MonotonicityNot monotonic
2022-01-10T17:30:28.181708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.48288
 
0.1%
15.58274
 
0.1%
15.28272
 
0.1%
15.14263
 
0.1%
15.5263
 
0.1%
15.56263
 
0.1%
15.29262
 
0.1%
15.74261
 
0.1%
15.6260
 
0.1%
15.78260
 
0.1%
Other values (5580)438898
99.4%
ValueCountFrequency (%)
-32.61
< 0.1%
-32.172
< 0.1%
-31.481
< 0.1%
-31.431
< 0.1%
-31.381
< 0.1%
-31.271
< 0.1%
-30.921
< 0.1%
-30.131
< 0.1%
-29.951
< 0.1%
-29.671
< 0.1%
ValueCountFrequency (%)
32.771
< 0.1%
32.271
< 0.1%
32.151
< 0.1%
32.121
< 0.1%
32.021
< 0.1%
31.921
< 0.1%
31.891
< 0.1%
31.871
< 0.1%
31.62
< 0.1%
31.581
< 0.1%

T2M_RANGE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1993
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.68584896
Minimum1.31
Maximum25.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-01-10T17:30:28.360251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.31
5-th percentile5.37
Q18.13
median10.82
Q313.09
95-th percentile15.96
Maximum25.52
Range24.21
Interquartile range (IQR)4.96

Descriptive statistics

Standard deviation3.284198776
Coefficient of variation (CV)0.3073409317
Kurtosis-0.6195457643
Mean10.68584896
Median Absolute Deviation (MAD)2.46
Skewness0.01725384315
Sum4718486.21
Variance10.7859616
MonotonicityNot monotonic
2022-01-10T17:30:28.558097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.16594
 
0.1%
11.92582
 
0.1%
11.38580
 
0.1%
12.66579
 
0.1%
10.82579
 
0.1%
11.58579
 
0.1%
11.64578
 
0.1%
11.98577
 
0.1%
11.42575
 
0.1%
11.9568
 
0.1%
Other values (1983)435773
98.7%
ValueCountFrequency (%)
1.311
< 0.1%
1.421
< 0.1%
1.741
< 0.1%
1.781
< 0.1%
1.792
< 0.1%
1.892
< 0.1%
1.91
< 0.1%
21
< 0.1%
2.022
< 0.1%
2.041
< 0.1%
ValueCountFrequency (%)
25.521
< 0.1%
25.411
< 0.1%
25.151
< 0.1%
24.81
< 0.1%
24.671
< 0.1%
24.311
< 0.1%
24.251
< 0.1%
24.141
< 0.1%
24.041
< 0.1%
23.991
< 0.1%

TS
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6049
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.81613112
Minimum-27.95
Maximum41.51
Zeros22
Zeros (%)< 0.1%
Negative31497
Negative (%)7.1%
Memory size3.4 MiB
2022-01-10T17:30:28.720404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-27.95
5-th percentile-3.5
Q19.41
median16.98
Q323.55
95-th percentile30.45
Maximum41.51
Range69.46
Interquartile range (IQR)14.14

Descriptive statistics

Standard deviation10.23411495
Coefficient of variation (CV)0.647068166
Kurtosis0.1070420044
Mean15.81613112
Median Absolute Deviation (MAD)7.06
Skewness-0.5618664484
Sum6983834.12
Variance104.7371089
MonotonicityNot monotonic
2022-01-10T17:30:28.899903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.3258
 
0.1%
19.34252
 
0.1%
17.26251
 
0.1%
18.9245
 
0.1%
18.92239
 
0.1%
19.58235
 
0.1%
18.58234
 
0.1%
19.2232
 
0.1%
19.94228
 
0.1%
19.15228
 
0.1%
Other values (6039)439162
99.5%
ValueCountFrequency (%)
-27.951
< 0.1%
-27.651
< 0.1%
-27.51
< 0.1%
-27.421
< 0.1%
-27.241
< 0.1%
-26.731
< 0.1%
-26.691
< 0.1%
-26.431
< 0.1%
-26.241
< 0.1%
-26.091
< 0.1%
ValueCountFrequency (%)
41.511
< 0.1%
41.431
< 0.1%
41.031
< 0.1%
41.021
< 0.1%
40.951
< 0.1%
40.921
< 0.1%
40.91
< 0.1%
40.681
< 0.1%
40.661
< 0.1%
40.542
< 0.1%

WS10M
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct815
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.406417597
Minimum0.47
Maximum11.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-01-10T17:30:29.060941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.47
5-th percentile1.53
Q11.9
median2.29
Q32.75
95-th percentile3.71
Maximum11.19
Range10.72
Interquartile range (IQR)0.85

Descriptive statistics

Standard deviation0.7333087166
Coefficient of variation (CV)0.3047304497
Kurtosis6.376247616
Mean2.406417597
Median Absolute Deviation (MAD)0.42
Skewness1.735169044
Sum1062587.38
Variance0.5377416739
MonotonicityNot monotonic
2022-01-10T17:30:29.229944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.923400
 
0.8%
1.963267
 
0.7%
23266
 
0.7%
1.93254
 
0.7%
1.983236
 
0.7%
1.843181
 
0.7%
2.043180
 
0.7%
1.883174
 
0.7%
1.943143
 
0.7%
1.863081
 
0.7%
Other values (805)409382
92.7%
ValueCountFrequency (%)
0.471
 
< 0.1%
0.523
< 0.1%
0.541
 
< 0.1%
0.621
 
< 0.1%
0.661
 
< 0.1%
0.671
 
< 0.1%
0.683
< 0.1%
0.693
< 0.1%
0.73
< 0.1%
0.712
< 0.1%
ValueCountFrequency (%)
11.191
< 0.1%
10.871
< 0.1%
10.821
< 0.1%
10.591
< 0.1%
10.381
< 0.1%
10.31
< 0.1%
10.192
< 0.1%
10.11
< 0.1%
10.011
< 0.1%
9.991
< 0.1%

WS10M_MAX
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1272
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.696424731
Minimum0.97
Maximum18.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-01-10T17:30:29.520155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.97
5-th percentile2.74
Q13.72
median4.52
Q35.47
95-th percentile7.22
Maximum18.16
Range17.19
Interquartile range (IQR)1.75

Descriptive statistics

Standard deviation1.414950128
Coefficient of variation (CV)0.3012824028
Kurtosis2.481967693
Mean4.696424731
Median Absolute Deviation (MAD)0.87
Skewness1.03362933
Sum2073772.09
Variance2.002083863
MonotonicityNot monotonic
2022-01-10T17:30:29.719392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.081608
 
0.4%
4.041586
 
0.4%
4.21572
 
0.4%
4.121561
 
0.4%
41560
 
0.4%
4.141560
 
0.4%
4.241557
 
0.4%
3.881553
 
0.4%
4.281552
 
0.4%
3.741540
 
0.3%
Other values (1262)425915
96.5%
ValueCountFrequency (%)
0.971
< 0.1%
11
< 0.1%
1.071
< 0.1%
1.091
< 0.1%
1.111
< 0.1%
1.171
< 0.1%
1.212
< 0.1%
1.252
< 0.1%
1.271
< 0.1%
1.291
< 0.1%
ValueCountFrequency (%)
18.161
< 0.1%
18.051
< 0.1%
17.661
< 0.1%
17.061
< 0.1%
17.021
< 0.1%
16.81
< 0.1%
16.781
< 0.1%
16.411
< 0.1%
16.091
< 0.1%
15.981
< 0.1%

WS10M_MIN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct556
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7090965749
Minimum0
Maximum7.34
Zeros29
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-01-10T17:30:29.872017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12
Q10.32
median0.56
Q30.94
95-th percentile1.79
Maximum7.34
Range7.34
Interquartile range (IQR)0.62

Descriptive statistics

Standard deviation0.5527669452
Coefficient of variation (CV)0.7795368992
Kurtosis5.648306795
Mean0.7090965749
Median Absolute Deviation (MAD)0.28
Skewness1.839393097
Sum313111.52
Variance0.3055512958
MonotonicityNot monotonic
2022-01-10T17:30:30.064220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.325534
 
1.3%
0.385465
 
1.2%
0.365408
 
1.2%
0.345375
 
1.2%
0.45334
 
1.2%
0.265184
 
1.2%
0.35155
 
1.2%
0.445055
 
1.1%
0.425007
 
1.1%
0.484969
 
1.1%
Other values (546)389078
88.1%
ValueCountFrequency (%)
029
 
< 0.1%
0.01292
 
0.1%
0.02673
 
0.2%
0.03976
 
0.2%
0.041256
0.3%
0.051499
0.3%
0.061796
0.4%
0.072068
0.5%
0.082406
0.5%
0.092550
0.6%
ValueCountFrequency (%)
7.341
< 0.1%
7.11
< 0.1%
6.991
< 0.1%
6.911
< 0.1%
6.81
< 0.1%
6.681
< 0.1%
6.611
< 0.1%
6.511
< 0.1%
6.461
< 0.1%
6.431
< 0.1%

WS10M_RANGE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1086
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.987323582
Minimum0.45
Maximum16.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-01-10T17:30:30.282724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.45
5-th percentile2.02
Q13.03
median3.87
Q34.82
95-th percentile6.33
Maximum16.58
Range16.13
Interquartile range (IQR)1.79

Descriptive statistics

Standard deviation1.338110186
Coefficient of variation (CV)0.3355910697
Kurtosis0.7470032005
Mean3.987323582
Median Absolute Deviation (MAD)0.89
Skewness0.6006772243
Sum1760658.55
Variance1.79053887
MonotonicityNot monotonic
2022-01-10T17:30:30.441098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.641552
 
0.4%
3.81536
 
0.3%
3.721528
 
0.3%
3.921526
 
0.3%
3.621526
 
0.3%
3.661524
 
0.3%
3.881519
 
0.3%
3.321516
 
0.3%
3.261513
 
0.3%
3.51510
 
0.3%
Other values (1076)426314
96.5%
ValueCountFrequency (%)
0.452
 
< 0.1%
0.471
 
< 0.1%
0.51
 
< 0.1%
0.521
 
< 0.1%
0.541
 
< 0.1%
0.562
 
< 0.1%
0.571
 
< 0.1%
0.583
< 0.1%
0.66
< 0.1%
0.611
 
< 0.1%
ValueCountFrequency (%)
16.581
< 0.1%
14.361
< 0.1%
14.081
< 0.1%
13.791
< 0.1%
13.531
< 0.1%
13.521
< 0.1%
13.261
< 0.1%
12.981
< 0.1%
12.811
< 0.1%
12.721
< 0.1%

WS50M
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1041
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.801491245
Minimum0.65
Maximum13.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-01-10T17:30:30.596714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.65
5-th percentile1.62
Q12.08
median2.55
Q33.21
95-th percentile4.91
Maximum13.6
Range12.95
Interquartile range (IQR)1.13

Descriptive statistics

Standard deviation1.081979737
Coefficient of variation (CV)0.3862156411
Kurtosis5.7675454
Mean2.801491245
Median Absolute Deviation (MAD)0.53
Skewness1.896975498
Sum1237037.68
Variance1.170680151
MonotonicityNot monotonic
2022-01-10T17:30:30.749698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.32720
 
0.6%
2.382650
 
0.6%
2.242644
 
0.6%
2.222629
 
0.6%
2.122627
 
0.6%
2.422621
 
0.6%
2.262600
 
0.6%
2.462587
 
0.6%
2.162562
 
0.6%
2.342555
 
0.6%
Other values (1031)415369
94.1%
ValueCountFrequency (%)
0.652
< 0.1%
0.683
< 0.1%
0.691
 
< 0.1%
0.71
 
< 0.1%
0.781
 
< 0.1%
0.82
< 0.1%
0.811
 
< 0.1%
0.821
 
< 0.1%
0.833
< 0.1%
0.841
 
< 0.1%
ValueCountFrequency (%)
13.61
< 0.1%
13.291
< 0.1%
131
< 0.1%
12.791
< 0.1%
12.711
< 0.1%
12.41
< 0.1%
12.22
< 0.1%
12.191
< 0.1%
12.141
< 0.1%
12.111
< 0.1%

WS50M_MAX
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1420
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.710053605
Minimum1.12
Maximum20.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-01-10T17:30:30.935692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.12
5-th percentile2.84
Q13.61
median4.37
Q35.4
95-th percentile7.8
Maximum20.18
Range19.06
Interquartile range (IQR)1.79

Descriptive statistics

Standard deviation1.600006172
Coefficient of variation (CV)0.3397002044
Kurtosis4.208463099
Mean4.710053605
Median Absolute Deviation (MAD)0.86
Skewness1.602009688
Sum2079790.11
Variance2.560019751
MonotonicityNot monotonic
2022-01-10T17:30:31.164963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.71676
 
0.4%
3.721673
 
0.4%
3.881656
 
0.4%
3.661641
 
0.4%
3.621621
 
0.4%
41619
 
0.4%
3.841617
 
0.4%
3.81616
 
0.4%
4.041616
 
0.4%
3.741608
 
0.4%
Other values (1410)425221
96.3%
ValueCountFrequency (%)
1.121
 
< 0.1%
1.211
 
< 0.1%
1.311
 
< 0.1%
1.372
< 0.1%
1.391
 
< 0.1%
1.421
 
< 0.1%
1.432
< 0.1%
1.462
< 0.1%
1.481
 
< 0.1%
1.493
< 0.1%
ValueCountFrequency (%)
20.181
< 0.1%
19.71
< 0.1%
19.471
< 0.1%
18.722
< 0.1%
18.671
< 0.1%
18.661
< 0.1%
18.111
< 0.1%
17.891
< 0.1%
17.761
< 0.1%
17.742
< 0.1%

WS50M_MIN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct809
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9915818998
Minimum0
Maximum10.61
Zeros33
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-01-10T17:30:31.346865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.16
Q10.41
median0.72
Q31.27
95-th percentile2.79
Maximum10.61
Range10.61
Interquartile range (IQR)0.86

Descriptive statistics

Standard deviation0.8785180427
Coefficient of variation (CV)0.8859762798
Kurtosis6.572836686
Mean0.9915818998
Median Absolute Deviation (MAD)0.38
Skewness2.136358157
Sum437846.87
Variance0.7717939513
MonotonicityNot monotonic
2022-01-10T17:30:31.536896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.444304
 
1.0%
0.44255
 
1.0%
0.384217
 
1.0%
0.424185
 
0.9%
0.464142
 
0.9%
0.364122
 
0.9%
0.54121
 
0.9%
0.484111
 
0.9%
0.524084
 
0.9%
0.344070
 
0.9%
Other values (799)399953
90.6%
ValueCountFrequency (%)
033
 
< 0.1%
0.01203
 
< 0.1%
0.02355
 
0.1%
0.03563
 
0.1%
0.04734
0.2%
0.05913
0.2%
0.061155
0.3%
0.071233
0.3%
0.081511
0.3%
0.091703
0.4%
ValueCountFrequency (%)
10.611
< 0.1%
10.381
< 0.1%
9.871
< 0.1%
9.761
< 0.1%
9.71
< 0.1%
9.561
< 0.1%
9.521
< 0.1%
9.341
< 0.1%
9.32
< 0.1%
9.292
< 0.1%

WS50M_RANGE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1155
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.718467266
Minimum0.46
Maximum18.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-01-10T17:30:31.711485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.46
5-th percentile1.98
Q12.79
median3.5
Q34.4
95-th percentile6.22
Maximum18.5
Range18.04
Interquartile range (IQR)1.61

Descriptive statistics

Standard deviation1.333855196
Coefficient of variation (CV)0.3587110226
Kurtosis2.432583045
Mean3.718467266
Median Absolute Deviation (MAD)0.78
Skewness1.15179622
Sum1641941.28
Variance1.779169683
MonotonicityNot monotonic
2022-01-10T17:30:31.881896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.081828
 
0.4%
2.91820
 
0.4%
3.041799
 
0.4%
31768
 
0.4%
2.781766
 
0.4%
3.061766
 
0.4%
2.841751
 
0.4%
2.921750
 
0.4%
3.11750
 
0.4%
3.141748
 
0.4%
Other values (1145)423818
96.0%
ValueCountFrequency (%)
0.462
< 0.1%
0.482
< 0.1%
0.541
 
< 0.1%
0.551
 
< 0.1%
0.572
< 0.1%
0.593
< 0.1%
0.62
< 0.1%
0.621
 
< 0.1%
0.633
< 0.1%
0.643
< 0.1%
ValueCountFrequency (%)
18.51
< 0.1%
15.351
< 0.1%
14.981
< 0.1%
14.731
< 0.1%
14.451
< 0.1%
13.731
< 0.1%
13.681
< 0.1%
13.671
< 0.1%
13.631
< 0.1%
13.61
< 0.1%

Interactions

2022-01-10T17:30:14.329675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:21.227245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:27.204580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:33.221845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:39.086061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:44.811664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:50.482327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:56.444423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:02.038719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:07.692185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:13.612774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:19.485886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:25.648663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:31.521826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:37.552320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:43.548236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:49.362939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:55.497731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:01.694958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:08.151995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:14.643241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:21.797627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:27.506167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:33.509677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:39.456257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:45.149091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:50.740117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:56.706178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:02.306298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:07.932457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:13.894919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:19.772730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:25.940298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:31.856620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:37.931686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:43.812812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:49.653730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:55.786147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:01.995356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:08.481957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:15.017319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:22.042935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:27.757232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:33.756148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:39.699381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:45.421732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:51.063263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:57.032963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:02.558868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:08.258521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:14.337405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:20.051203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:26.245681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:32.103529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:38.190168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:44.097402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:50.085177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:56.061079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:02.303141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:08.753723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:15.323057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:22.401297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:28.011455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:34.026373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:39.932046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:45.672491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:51.580068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:57.281528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:02.803321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:08.569867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:14.650409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:20.442800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:26.548189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:32.442492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:38.472295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:44.390123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:50.407733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:56.367553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:02.579919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:09.094118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:15.585499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:22.662117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:28.374778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:34.295405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:40.225606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:45.917241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:51.828082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:57.558587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:03.062192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:08.851067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:14.896192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:20.745323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:26.801278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:32.715414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:38.764308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:44.722199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:50.665439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:56.688647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:02.964488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:09.363557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:15.881535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:22.911830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:28.701859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:34.596955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:40.471144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:46.160871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:52.101933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:57.825557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:03.306755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:09.134716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:15.149499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:21.063173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:27.085129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:32.989481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:39.061981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:44.962260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:50.925025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:57.006342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:03.320095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:09.656040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:16.242440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:23.267785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:28.952328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:34.870515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:40.734952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:46.481823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:52.358719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:58.128500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:03.548664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:09.403195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:15.449127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:21.387876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:27.362014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:33.304343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:39.349246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:45.216875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:51.284856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:57.379061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:03.628087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:09.969821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:16.617215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:23.633634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:29.274873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:35.165565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:41.036778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:46.753272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:52.617717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:58.405195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:03.803009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:09.661718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:15.725763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:21.733146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:27.618380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:33.572247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:39.678872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:45.468104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:51.564845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:57.659115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:03.954522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:10.212902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:16.972165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:23.880697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:29.757351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:35.434778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:41.309347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:47.035305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:52.876286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:58.753391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:04.083457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:09.947421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:15.997952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:22.029875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:28.017281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:33.870659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:39.933268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:45.804603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:51.844794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:57.968435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:04.255388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:10.546963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:17.275446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:24.153950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:30.068386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:35.719190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:41.563337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:47.358119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:53.129762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:59.028997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:04.373222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:10.233243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:16.247587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:22.339134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:28.302183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:34.124857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:40.230460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:46.045640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:52.103061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:58.248263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:04.569391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:10.837719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:17.699369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:24.399885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:30.433282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:36.000177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:41.825418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:47.622651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:53.389501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:59.296336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:04.683553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:10.525732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:16.585742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:22.736290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:28.580556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:34.424647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:40.541612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:46.344678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:52.369732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:58.491527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:04.998170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:11.121814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:18.047576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:24.676856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:30.683760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:36.249851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:42.090291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:47.877668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:53.758705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:59.559661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:05.013939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:10.807976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:16.859246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:22.998003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:28.824560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:34.761467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:40.809976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:46.687320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:52.690150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:58.748554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:05.371428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:11.367091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:18.399828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:24.971007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:30.938468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:36.600521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:42.388176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:48.181286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:54.149933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:59.825957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:05.347846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:11.061961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:17.150204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:23.270672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:29.090378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:35.103379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:41.100329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:46.990554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:53.011481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:59.067404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:05.743958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:11.696707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:18.730563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:25.254621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:31.215274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:36.907475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:42.646744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:48.481765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:54.422040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:00.127651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:05.691723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:11.359313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:17.505239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:23.593839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:29.386628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:35.470854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:41.385351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:47.273333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:53.322042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:59.381949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:06.018426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:11.996750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:19.106686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:25.519410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:31.480909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:37.180897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:42.923590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:48.775669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:54.699265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:00.427615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:05.968418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:11.678242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:17.790835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:23.873145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:29.673976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:35.734211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:41.666898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:47.566532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:53.610565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:59.704193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:06.392396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:12.332233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:19.505124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:25.845483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:31.754163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:37.576759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:43.190017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:49.061446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:54.986731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:00.679401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:06.234647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:11.986740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:18.068963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:24.185187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:29.986750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:36.033345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:41.986427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:47.888037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:53.933492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:00.075594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:06.670695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:12.839727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:19.835642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:26.106834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:32.036204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:37.873358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:43.461498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:49.356224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:55.283051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:00.958324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:06.553632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:12.280117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:18.368221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:24.444025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:30.320885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:36.338025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:42.272262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:48.179380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:54.279109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:00.405482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:06.941139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:13.098022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:20.104870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:26.359095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:32.312047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:38.166600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:43.827934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:49.619272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:55.582280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:01.267020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:06.846781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:12.575348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:18.631781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:24.751921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:30.621387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:36.627088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:42.537446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:48.455919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:54.595231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:00.672479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:07.239058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:13.365150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:20.435567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:26.664399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:32.573971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:38.491974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:44.094239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:49.894863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:55.890271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:01.534595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:07.099775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:12.949544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:18.887299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:25.039047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:30.920841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:36.922685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:42.954002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:48.774837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:54.901127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:00.957948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:07.569976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:13.712366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:20.904284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:26.934014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:32.912423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:38.754225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:44.452878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:50.212430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:28:56.184068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:01.782482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:07.379876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:13.242419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:19.177903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:25.383897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:31.234686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:37.202141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:43.255903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:49.084501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:29:55.182172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:01.343858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:07.882019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-10T17:30:14.069436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-01-10T17:30:32.336437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-10T17:30:32.543194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-10T17:30:32.811158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-10T17:30:33.142255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-10T17:30:21.256608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-10T17:30:22.189678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

DATEDISTRICTLATLONPRECTOTPSQV2MRH2MT2MT2MWETT2M_MAXT2M_MINT2M_RANGETSWS10MWS10M_MAXWS10M_MINWS10M_RANGEWS50MWS50M_MAXWS50M_MINWS50M_RANGE
01981-01-01Lamjung28.384.40.0077.593.4749.085.11-5.1812.150.2511.902.431.763.680.263.411.602.830.182.65
11981-01-02Lamjung28.384.40.0077.653.4448.545.15-5.5611.101.719.392.081.653.710.173.541.462.720.142.58
21981-01-03Lamjung28.384.40.0077.603.8756.974.55-3.949.790.769.042.551.633.950.323.631.573.110.452.66
31981-01-04Lamjung28.384.40.0077.493.9564.393.06-3.608.74-0.419.152.121.813.900.243.661.803.070.302.76
41981-01-05Lamjung28.384.40.6777.484.1368.272.87-2.997.51-0.067.571.831.653.900.213.691.663.300.293.01
51981-01-06Lamjung28.384.40.4777.363.9066.422.45-4.128.01-0.678.681.321.934.570.603.971.903.810.283.53
61981-01-07Lamjung28.384.42.2877.324.3583.940.70-2.603.91-1.595.500.371.332.760.082.681.202.030.101.93
71981-01-08Lamjung28.384.40.5577.433.5467.890.81-5.456.40-2.378.77-0.671.723.970.143.831.552.960.222.74
81981-01-09Lamjung28.384.40.0077.693.2862.480.92-6.206.94-3.2210.16-0.701.834.160.223.931.623.100.262.83
91981-01-10Lamjung28.384.40.0077.893.1555.042.19-6.557.89-1.279.16-0.271.543.800.053.751.372.840.202.64

Last rows

DATEDISTRICTLATLONPRECTOTPSQV2MRH2MT2MT2MWETT2M_MAXT2M_MINT2M_RANGETSWS10MWS10M_MAXWS10M_MINWS10M_RANGEWS50MWS50M_MAXWS50M_MINWS50M_RANGE
4415542019-12-22Udayapur26.986.50.095.576.8173.8212.087.4619.047.4611.5811.281.955.450.125.332.576.250.146.11
4415552019-12-23Udayapur26.986.50.095.666.4165.9712.906.4819.838.3511.4811.231.844.200.423.792.474.700.554.15
4415562019-12-24Udayapur26.986.50.095.734.9851.8312.762.9919.698.5611.1310.572.163.401.052.353.064.001.482.52
4415572019-12-25Udayapur26.986.50.095.635.2757.1412.113.5818.827.8011.0310.081.813.700.353.352.434.020.333.69
4415582019-12-26Udayapur26.986.50.095.594.9252.8912.232.7118.818.3410.4710.161.792.790.871.922.282.930.932.00
4415592019-12-27Udayapur26.986.50.095.524.7254.6011.132.1717.986.9711.019.732.384.201.252.943.404.571.373.20
4415602019-12-28Udayapur26.986.50.095.734.2350.9010.560.6018.525.3413.189.072.032.521.071.453.054.511.443.07
4415612019-12-29Udayapur26.986.50.095.764.0846.0911.510.1819.347.0412.309.122.013.010.852.172.974.120.943.18
4415622019-12-30Udayapur26.986.50.095.844.4447.0712.471.3220.037.8312.209.761.441.940.271.672.003.220.153.06
4415632019-12-31Udayapur26.986.50.095.935.4349.6814.773.8921.9610.2811.6811.671.593.220.452.782.023.430.662.77